OBJECTIVES We sought to evaluate the correlation between MRI phenotypes of prostate cancer as defined by PI-RADS v2 and the Decipher Genomic Classifier (used to estimate the risk of early metastases). METHODS This single-center, retrospective study included 72 nonconsecutive men with prostate cancer who underwent MRI before radical prostatectomy performed between April 2014 and August 2017 and whose MRI registered lesions were microdissected from radical prostatectomy specimens and then profiled using Decipher (89 lesions; 23 MRI invisible [PI-RADS v2 scores ≤ 2] and 66 MRI visible [PI-RADS v2 scores ≥ 3]). Linear regression analysis was used to assess clinicopathologic and MRI predictors of Decipher results; correlation coefficients (r) were used to quantify these associations. AUC was used to determine whether PI-RADS v2 could accurately distinguish between low-risk (Decipher score < 0.45) and intermediate-/high-risk (Decipher score ≥ 0.45) lesions. RESULTS MRI-visible lesions had higher Decipher scores than MRI-invisible lesions (mean difference 0.22; 95% CI 0.13, 0.32; p < 0.0001); most MRI-invisible lesions (82.6%) were low risk. PI-RADS v2 had moderate correlation with Decipher (r = 0.54) and had higher accuracy (AUC 0.863) than prostate cancer grade groups (AUC 0.780) in peripheral zone lesions (95% CI for difference 0.01, 0.15; p = 0.018). CONCLUSIONS MRI phenotypes of prostate cancer are positively correlated with Decipher risk groups. Although PI-RADS v2 can accurately distinguish between lesions classified by Decipher as low or intermediate/high risk, some lesions classified as intermediate/high risk by Decipher are invisible on MRI. KEY POINTS • MRI phenotypes of prostate cancer as defined by PI-RADS v2 positively correlated with a genomic classifier that estimates the risk of early metastases. • Most but not all MRI-invisible lesions had a low risk for early metastases according to the genomic classifier. • MRI could be used in conjunction with genomic assays to identify lesions that may carry biological potential for early metastases.

中文翻译：

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MRI表型与前列腺癌的基因组分类器之间的相关性：初步发现。

目的我们试图评估由PI-RADS v2定义的前列腺癌的MRI表型与解密基因组分类器（用于估计早期转移的风险）之间的相关性。方法这项单中心回顾性研究纳入了72例前列腺癌非连续性男性，这些男性在2014年4月至2017年8月进行根治性前列腺切除术之前接受了MRI检查，并从根治性前列腺切除术标本中显微解剖了MRI记录的病变，然后使用Decipher进行了轮廓分析（89个病变; 23 MRI不可见[PI-RADS v2分数≤2]和66 MRI可见[PI-RADS v2分数≥3]）。线性回归分析用于评估解密结果的临床病理和MRI预测因子；相关系数（r）用于量化这些关联。使用AUC确定PI-RADS v2是否可以准确地区分低风险（解密评分<0.45）和中/高风险（解密评分≥0.45）病变。结果MRI可见病灶的Decipher评分高于MRI可见病灶（均差0.22； 95％CI 0.13，0.32； p <0.0001）。大多数MRI看不见的病变（82.6％）具有低风险。PI-RADS v2与Decipher具有适度的相关性（r = 0.54），并且在前列腺癌周围组病变中的准确度（AUC 0.863）比前列腺癌分级组（AUC 0.780）高（95％CI的差异0.01、0.15； p = 0.018）。结论前列腺癌的MRI表型与Decipher风险组呈正相关。尽管PI-RADS v2可以准确地区分由Decipher分类为低风险或中/高风险的病变，在MRI上看不到被Decipher分类为中/高风险的某些病变。要点•PI-RADS v2定义的前列腺癌的MRI表型与估计早期转移风险的基因组分类器正相关。•根据基因组分类器，大多数（但不是全部）MRI看不见的病变发生早期转移的风险较低。•MRI可以与基因组分析结合使用，以识别可能具有早期转移生物学潜力的病变。